Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations2823
Missing cells5157
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory551.5 KiB
Average record size in memory200.0 B

Variable types

Numeric7
DateTime1
Categorical9
Text8

Alerts

ADDRESSLINE2 is highly overall correlated with COUNTRY and 4 other fieldsHigh correlation
COUNTRY is highly overall correlated with ADDRESSLINE2 and 2 other fieldsHigh correlation
DEALSIZE is highly overall correlated with SALESHigh correlation
MONTH_ID is highly overall correlated with QTR_IDHigh correlation
MSRP is highly overall correlated with PRICEEACH and 1 other fieldsHigh correlation
ORDERNUMBER is highly overall correlated with ADDRESSLINE2 and 2 other fieldsHigh correlation
PRICEEACH is highly overall correlated with MSRP and 1 other fieldsHigh correlation
QTR_ID is highly overall correlated with MONTH_ID and 1 other fieldsHigh correlation
QUANTITYORDERED is highly overall correlated with SALESHigh correlation
SALES is highly overall correlated with DEALSIZE and 3 other fieldsHigh correlation
STATE is highly overall correlated with ADDRESSLINE2 and 2 other fieldsHigh correlation
TERRITORY is highly overall correlated with ADDRESSLINE2 and 2 other fieldsHigh correlation
YEAR_ID is highly overall correlated with ADDRESSLINE2 and 1 other fieldsHigh correlation
STATUS is highly imbalanced (79.2%) Imbalance
ADDRESSLINE2 has 2521 (89.3%) missing values Missing
STATE has 1486 (52.6%) missing values Missing
POSTALCODE has 76 (2.7%) missing values Missing
TERRITORY has 1074 (38.0%) missing values Missing

Reproduction

Analysis started2025-08-08 10:22:59.047667
Analysis finished2025-08-08 10:23:06.178605
Duration7.13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ORDERNUMBER
Real number (ℝ)

High correlation 

Distinct307
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10258.725
Minimum10100
Maximum10425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:06.392020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10115
Q110180
median10262
Q310333.5
95-th percentile10405
Maximum10425
Range325
Interquartile range (IQR)153.5

Descriptive statistics

Standard deviation92.085478
Coefficient of variation (CV)0.0089763081
Kurtosis-1.1733092
Mean10258.725
Median Absolute Deviation (MAD)79
Skewness0.013822989
Sum28960381
Variance8479.7352
MonotonicityNot monotonic
2025-08-08T15:53:06.524145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10386 18
 
0.6%
10398 18
 
0.6%
10222 18
 
0.6%
10106 18
 
0.6%
10159 18
 
0.6%
10168 18
 
0.6%
10332 18
 
0.6%
10275 18
 
0.6%
10165 18
 
0.6%
10316 18
 
0.6%
Other values (297) 2643
93.6%
ValueCountFrequency (%)
10100 4
 
0.1%
10101 4
 
0.1%
10102 2
 
0.1%
10103 16
0.6%
10104 13
0.5%
10105 15
0.5%
10106 18
0.6%
10107 8
0.3%
10108 16
0.6%
10109 6
 
0.2%
ValueCountFrequency (%)
10425 13
0.5%
10424 6
0.2%
10423 5
 
0.2%
10422 2
 
0.1%
10421 2
 
0.1%
10420 13
0.5%
10419 14
0.5%
10417 6
0.2%
10416 14
0.5%
10415 5
 
0.2%

QUANTITYORDERED
Real number (ℝ)

High correlation 

Distinct58
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.092809
Minimum6
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:06.681460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21
Q127
median35
Q343
95-th percentile49
Maximum97
Range91
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.7414427
Coefficient of variation (CV)0.27759085
Kurtosis0.41574379
Mean35.092809
Median Absolute Deviation (MAD)8
Skewness0.36258533
Sum99067
Variance94.895707
MonotonicityNot monotonic
2025-08-08T15:53:06.823695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 112
 
4.0%
21 103
 
3.6%
46 101
 
3.6%
27 100
 
3.5%
45 97
 
3.4%
41 97
 
3.4%
31 97
 
3.4%
26 96
 
3.4%
29 94
 
3.3%
48 94
 
3.3%
Other values (48) 1832
64.9%
ValueCountFrequency (%)
6 2
 
0.1%
10 2
 
0.1%
11 2
 
0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 4
 
0.1%
16 1
 
< 0.1%
18 3
 
0.1%
19 3
 
0.1%
20 93
3.3%
ValueCountFrequency (%)
97 1
 
< 0.1%
85 1
 
< 0.1%
77 1
 
< 0.1%
76 3
0.1%
70 2
 
0.1%
66 5
0.2%
65 1
 
< 0.1%
64 3
0.1%
62 1
 
< 0.1%
61 3
0.1%

PRICEEACH
Real number (ℝ)

High correlation 

Distinct1016
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.658544
Minimum26.88
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:06.950064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.88
5-th percentile42.67
Q168.86
median95.7
Q3100
95-th percentile100
Maximum100
Range73.12
Interquartile range (IQR)31.14

Descriptive statistics

Standard deviation20.174277
Coefficient of variation (CV)0.24115022
Kurtosis-0.37481769
Mean83.658544
Median Absolute Deviation (MAD)4.3
Skewness-0.94664886
Sum236168.07
Variance407.00143
MonotonicityNot monotonic
2025-08-08T15:53:07.075971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1304
46.2%
96.34 6
 
0.2%
59.87 6
 
0.2%
89.38 5
 
0.2%
90.17 5
 
0.2%
67.14 5
 
0.2%
57.73 5
 
0.2%
61.99 5
 
0.2%
51.93 5
 
0.2%
80.55 5
 
0.2%
Other values (1006) 1472
52.1%
ValueCountFrequency (%)
26.88 1
 
< 0.1%
27.22 1
 
< 0.1%
28.29 1
 
< 0.1%
28.88 1
 
< 0.1%
29.21 2
0.1%
29.54 3
0.1%
29.7 1
 
< 0.1%
29.87 1
 
< 0.1%
30.06 2
0.1%
30.2 1
 
< 0.1%
ValueCountFrequency (%)
100 1304
46.2%
99.91 1
 
< 0.1%
99.82 2
 
0.1%
99.72 1
 
< 0.1%
99.69 1
 
< 0.1%
99.67 1
 
< 0.1%
99.66 1
 
< 0.1%
99.58 1
 
< 0.1%
99.57 1
 
< 0.1%
99.55 2
 
0.1%

ORDERLINENUMBER
Real number (ℝ)

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4661707
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:07.170282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile14
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.225841
Coefficient of variation (CV)0.65353068
Kurtosis-0.56115424
Mean6.4661707
Median Absolute Deviation (MAD)3
Skewness0.59074121
Sum18254
Variance17.857732
MonotonicityNot monotonic
2025-08-08T15:53:07.248512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 307
10.9%
2 291
10.3%
3 270
9.6%
4 256
9.1%
5 239
8.5%
6 221
7.8%
7 197
 
7.0%
8 187
 
6.6%
9 165
 
5.8%
10 141
 
5.0%
Other values (8) 549
19.4%
ValueCountFrequency (%)
1 307
10.9%
2 291
10.3%
3 270
9.6%
4 256
9.1%
5 239
8.5%
6 221
7.8%
7 197
7.0%
8 187
6.6%
9 165
5.8%
10 141
5.0%
ValueCountFrequency (%)
18 10
 
0.4%
17 25
 
0.9%
16 42
 
1.5%
15 56
 
2.0%
14 81
2.9%
13 97
3.4%
12 110
3.9%
11 128
4.5%
10 141
5.0%
9 165
5.8%

SALES
Real number (ℝ)

High correlation 

Distinct2763
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3553.8891
Minimum482.13
Maximum14082.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:07.358791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum482.13
5-th percentile1268.757
Q12203.43
median3184.8
Q34508
95-th percentile7108.12
Maximum14082.8
Range13600.67
Interquartile range (IQR)2304.57

Descriptive statistics

Standard deviation1841.8651
Coefficient of variation (CV)0.51826747
Kurtosis1.7926765
Mean3553.8891
Median Absolute Deviation (MAD)1102.31
Skewness1.161076
Sum10032629
Variance3392467.1
MonotonicityNot monotonic
2025-08-08T15:53:07.500292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3003 3
 
0.1%
4181.44 2
 
0.1%
2851.54 2
 
0.1%
4428 2
 
0.1%
2559.6 2
 
0.1%
3508.8 2
 
0.1%
2702.04 2
 
0.1%
5182 2
 
0.1%
2173.6 2
 
0.1%
3451 2
 
0.1%
Other values (2753) 2802
99.3%
ValueCountFrequency (%)
482.13 1
< 0.1%
541.14 1
< 0.1%
553.95 1
< 0.1%
577.6 1
< 0.1%
640.05 1
< 0.1%
651.8 1
< 0.1%
652.35 1
< 0.1%
683.8 1
< 0.1%
694.6 1
< 0.1%
703.6 1
< 0.1%
ValueCountFrequency (%)
14082.8 1
< 0.1%
12536.5 1
< 0.1%
12001 1
< 0.1%
11887.8 1
< 0.1%
11886.6 1
< 0.1%
11739.7 1
< 0.1%
11623.7 1
< 0.1%
11336.7 1
< 0.1%
11279.2 1
< 0.1%
10993.5 1
< 0.1%
Distinct252
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
Minimum2003-01-06 00:00:00
Maximum2005-05-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-08T15:53:07.627036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:07.778740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

STATUS
Categorical

Imbalance 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
Shipped
2617 
Cancelled
 
60
Resolved
 
47
On Hold
 
44
In Process
 
41

Length

Max length10
Median length7
Mean length7.1076869
Min length7

Characters and Unicode

Total characters20065
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShipped
2nd rowShipped
3rd rowShipped
4th rowShipped
5th rowShipped

Common Values

ValueCountFrequency (%)
Shipped 2617
92.7%
Cancelled 60
 
2.1%
Resolved 47
 
1.7%
On Hold 44
 
1.6%
In Process 41
 
1.5%
Disputed 14
 
0.5%

Length

2025-08-08T15:53:07.895892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:08.000175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
shipped 2617
90.0%
cancelled 60
 
2.1%
resolved 47
 
1.6%
on 44
 
1.5%
hold 44
 
1.5%
in 41
 
1.4%
process 41
 
1.4%
disputed 14
 
0.5%

Most occurring characters

ValueCountFrequency (%)
p 5248
26.2%
e 2886
14.4%
d 2782
13.9%
i 2631
13.1%
S 2617
13.0%
h 2617
13.0%
l 211
 
1.1%
n 145
 
0.7%
s 143
 
0.7%
o 132
 
0.7%
Other values (14) 653
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 5248
26.2%
e 2886
14.4%
d 2782
13.9%
i 2631
13.1%
S 2617
13.0%
h 2617
13.0%
l 211
 
1.1%
n 145
 
0.7%
s 143
 
0.7%
o 132
 
0.7%
Other values (14) 653
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 5248
26.2%
e 2886
14.4%
d 2782
13.9%
i 2631
13.1%
S 2617
13.0%
h 2617
13.0%
l 211
 
1.1%
n 145
 
0.7%
s 143
 
0.7%
o 132
 
0.7%
Other values (14) 653
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 5248
26.2%
e 2886
14.4%
d 2782
13.9%
i 2631
13.1%
S 2617
13.0%
h 2617
13.0%
l 211
 
1.1%
n 145
 
0.7%
s 143
 
0.7%
o 132
 
0.7%
Other values (14) 653
 
3.3%

QTR_ID
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
4
1094 
1
665 
2
561 
3
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2823
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

Length

2025-08-08T15:53:08.084782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:08.157895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

Most occurring characters

ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1094
38.8%
1 665
23.6%
2 561
19.9%
3 503
17.8%

MONTH_ID
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0924548
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:08.226889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6566333
Coefficient of variation (CV)0.51556667
Kurtosis-1.3832748
Mean7.0924548
Median Absolute Deviation (MAD)3
Skewness-0.27290156
Sum20022
Variance13.370967
MonotonicityNot monotonic
2025-08-08T15:53:08.331143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 597
21.1%
10 317
11.2%
5 252
8.9%
1 229
 
8.1%
2 224
 
7.9%
3 212
 
7.5%
8 191
 
6.8%
12 180
 
6.4%
4 178
 
6.3%
9 171
 
6.1%
Other values (2) 272
9.6%
ValueCountFrequency (%)
1 229
8.1%
2 224
7.9%
3 212
7.5%
4 178
6.3%
5 252
8.9%
6 131
4.6%
7 141
5.0%
8 191
6.8%
9 171
6.1%
10 317
11.2%
ValueCountFrequency (%)
12 180
 
6.4%
11 597
21.1%
10 317
11.2%
9 171
 
6.1%
8 191
 
6.8%
7 141
 
5.0%
6 131
 
4.6%
5 252
8.9%
4 178
 
6.3%
3 212
 
7.5%

YEAR_ID
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2004
1345 
2003
1000 
2005
478 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11292
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2003
2nd row2003
3rd row2003
4th row2003
5th row2003

Common Values

ValueCountFrequency (%)
2004 1345
47.6%
2003 1000
35.4%
2005 478
 
16.9%

Length

2025-08-08T15:53:08.415801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:08.478780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2004 1345
47.6%
2003 1000
35.4%
2005 478
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 5646
50.0%
2 2823
25.0%
4 1345
 
11.9%
3 1000
 
8.9%
5 478
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5646
50.0%
2 2823
25.0%
4 1345
 
11.9%
3 1000
 
8.9%
5 478
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5646
50.0%
2 2823
25.0%
4 1345
 
11.9%
3 1000
 
8.9%
5 478
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5646
50.0%
2 2823
25.0%
4 1345
 
11.9%
3 1000
 
8.9%
5 478
 
4.2%

PRODUCTLINE
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
Classic Cars
967 
Vintage Cars
607 
Motorcycles
331 
Planes
306 
Trucks and Buses
301 
Other values (2)
311 

Length

Max length16
Median length12
Mean length10.914984
Min length5

Characters and Unicode

Total characters30813
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotorcycles
2nd rowMotorcycles
3rd rowMotorcycles
4th rowMotorcycles
5th rowMotorcycles

Common Values

ValueCountFrequency (%)
Classic Cars 967
34.3%
Vintage Cars 607
21.5%
Motorcycles 331
 
11.7%
Planes 306
 
10.8%
Trucks and Buses 301
 
10.7%
Ships 234
 
8.3%
Trains 77
 
2.7%

Length

2025-08-08T15:53:08.557899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:08.662701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cars 1574
31.5%
classic 967
19.3%
vintage 607
 
12.1%
motorcycles 331
 
6.6%
planes 306
 
6.1%
trucks 301
 
6.0%
and 301
 
6.0%
buses 301
 
6.0%
ships 234
 
4.7%
trains 77
 
1.5%

Most occurring characters

ValueCountFrequency (%)
s 5359
17.4%
a 3832
12.4%
C 2541
 
8.2%
r 2283
 
7.4%
2176
 
7.1%
c 1930
 
6.3%
i 1885
 
6.1%
l 1604
 
5.2%
e 1545
 
5.0%
n 1291
 
4.2%
Other values (15) 6367
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 5359
17.4%
a 3832
12.4%
C 2541
 
8.2%
r 2283
 
7.4%
2176
 
7.1%
c 1930
 
6.3%
i 1885
 
6.1%
l 1604
 
5.2%
e 1545
 
5.0%
n 1291
 
4.2%
Other values (15) 6367
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 5359
17.4%
a 3832
12.4%
C 2541
 
8.2%
r 2283
 
7.4%
2176
 
7.1%
c 1930
 
6.3%
i 1885
 
6.1%
l 1604
 
5.2%
e 1545
 
5.0%
n 1291
 
4.2%
Other values (15) 6367
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 5359
17.4%
a 3832
12.4%
C 2541
 
8.2%
r 2283
 
7.4%
2176
 
7.1%
c 1930
 
6.3%
i 1885
 
6.1%
l 1604
 
5.2%
e 1545
 
5.0%
n 1291
 
4.2%
Other values (15) 6367
20.7%

MSRP
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.71555
Minimum33
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:08.877284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile43
Q168
median99
Q3124
95-th percentile170
Maximum214
Range181
Interquartile range (IQR)56

Descriptive statistics

Standard deviation40.187912
Coefficient of variation (CV)0.3990239
Kurtosis-0.13181452
Mean100.71555
Median Absolute Deviation (MAD)28
Skewness0.58017505
Sum284320
Variance1615.0682
MonotonicityNot monotonic
2025-08-08T15:53:09.008914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 104
 
3.7%
99 103
 
3.6%
136 80
 
2.8%
62 78
 
2.8%
68 77
 
2.7%
60 76
 
2.7%
80 73
 
2.6%
115 54
 
1.9%
101 54
 
1.9%
54 54
 
1.9%
Other values (70) 2070
73.3%
ValueCountFrequency (%)
33 25
0.9%
35 28
1.0%
37 27
1.0%
40 25
0.9%
41 22
0.8%
43 26
0.9%
44 25
0.9%
49 27
1.0%
50 51
1.8%
53 26
0.9%
ValueCountFrequency (%)
214 28
1.0%
207 26
0.9%
194 25
0.9%
193 26
0.9%
173 26
0.9%
170 22
0.8%
169 52
1.8%
168 26
0.9%
163 27
1.0%
157 26
0.9%
Distinct109
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:09.267361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.110875
Min length8

Characters and Unicode

Total characters22897
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS10_1678
2nd rowS10_1678
3rd rowS10_1678
4th rowS10_1678
5th rowS10_1678
ValueCountFrequency (%)
s18_3232 52
 
1.8%
s12_1666 28
 
1.0%
s10_4962 28
 
1.0%
s18_1097 28
 
1.0%
s10_1949 28
 
1.0%
s18_2432 28
 
1.0%
s32_2509 28
 
1.0%
s24_2840 28
 
1.0%
s24_1444 28
 
1.0%
s50_1392 28
 
1.0%
Other values (99) 2519
89.2%
2025-08-08T15:53:09.581529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3209
14.0%
1 2854
12.5%
_ 2823
12.3%
S 2823
12.3%
4 2015
8.8%
8 2007
8.8%
0 1799
7.9%
3 1713
7.5%
7 1075
 
4.7%
9 949
 
4.1%
Other values (2) 1630
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3209
14.0%
1 2854
12.5%
_ 2823
12.3%
S 2823
12.3%
4 2015
8.8%
8 2007
8.8%
0 1799
7.9%
3 1713
7.5%
7 1075
 
4.7%
9 949
 
4.1%
Other values (2) 1630
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3209
14.0%
1 2854
12.5%
_ 2823
12.3%
S 2823
12.3%
4 2015
8.8%
8 2007
8.8%
0 1799
7.9%
3 1713
7.5%
7 1075
 
4.7%
9 949
 
4.1%
Other values (2) 1630
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3209
14.0%
1 2854
12.5%
_ 2823
12.3%
S 2823
12.3%
4 2015
8.8%
8 2007
8.8%
0 1799
7.9%
3 1713
7.5%
7 1075
 
4.7%
9 949
 
4.1%
Other values (2) 1630
7.1%
Distinct92
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:09.819052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length26
Mean length20.972724
Min length10

Characters and Unicode

Total characters59206
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLand of Toys Inc.
2nd rowReims Collectables
3rd rowLyon Souveniers
4th rowToys4GrownUps.com
5th rowCorporate Gift Ideas Co.
ValueCountFrequency (%)
co 665
 
7.6%
ltd 544
 
6.2%
gifts 410
 
4.7%
mini 393
 
4.5%
inc 388
 
4.4%
collectables 334
 
3.8%
shopping 281
 
3.2%
euro 259
 
2.9%
channel 259
 
2.9%
gift 237
 
2.7%
Other values (136) 5023
57.1%
2025-08-08T15:53:10.196235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5970
 
10.1%
o 4285
 
7.2%
i 4157
 
7.0%
e 4015
 
6.8%
s 3647
 
6.2%
n 3469
 
5.9%
t 3444
 
5.8%
a 3083
 
5.2%
l 2886
 
4.9%
r 2405
 
4.1%
Other values (45) 21845
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5970
 
10.1%
o 4285
 
7.2%
i 4157
 
7.0%
e 4015
 
6.8%
s 3647
 
6.2%
n 3469
 
5.9%
t 3444
 
5.8%
a 3083
 
5.2%
l 2886
 
4.9%
r 2405
 
4.1%
Other values (45) 21845
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5970
 
10.1%
o 4285
 
7.2%
i 4157
 
7.0%
e 4015
 
6.8%
s 3647
 
6.2%
n 3469
 
5.9%
t 3444
 
5.8%
a 3083
 
5.2%
l 2886
 
4.9%
r 2405
 
4.1%
Other values (45) 21845
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5970
 
10.1%
o 4285
 
7.2%
i 4157
 
7.0%
e 4015
 
6.8%
s 3647
 
6.2%
n 3469
 
5.9%
t 3444
 
5.8%
a 3083
 
5.2%
l 2886
 
4.9%
r 2405
 
4.1%
Other values (45) 21845
36.9%

PHONE
Text

Distinct91
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:10.432867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length10
Mean length11.636557
Min length9

Characters and Unicode

Total characters32850
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2125557818
2nd row26.47.1555
3rd row+33 1 46 62 7555
4th row6265557265
5th row6505551386
ValueCountFrequency (%)
555 375
 
6.7%
91 291
 
5.2%
94 259
 
4.6%
44 259
 
4.6%
4155551450 180
 
3.2%
8555 122
 
2.2%
171 118
 
2.1%
3555 82
 
1.5%
65 79
 
1.4%
4555 78
 
1.4%
Other values (127) 3750
67.0%
2025-08-08T15:53:10.888846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10957
33.4%
2770
 
8.4%
4 2554
 
7.8%
1 2528
 
7.7%
2 2161
 
6.6%
9 1685
 
5.1%
6 1685
 
5.1%
0 1623
 
4.9%
8 1476
 
4.5%
3 1285
 
3.9%
Other values (6) 4126
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 10957
33.4%
2770
 
8.4%
4 2554
 
7.8%
1 2528
 
7.7%
2 2161
 
6.6%
9 1685
 
5.1%
6 1685
 
5.1%
0 1623
 
4.9%
8 1476
 
4.5%
3 1285
 
3.9%
Other values (6) 4126
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 10957
33.4%
2770
 
8.4%
4 2554
 
7.8%
1 2528
 
7.7%
2 2161
 
6.6%
9 1685
 
5.1%
6 1685
 
5.1%
0 1623
 
4.9%
8 1476
 
4.5%
3 1285
 
3.9%
Other values (6) 4126
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 10957
33.4%
2770
 
8.4%
4 2554
 
7.8%
1 2528
 
7.7%
2 2161
 
6.6%
9 1685
 
5.1%
6 1685
 
5.1%
0 1623
 
4.9%
8 1476
 
4.5%
3 1285
 
3.9%
Other values (6) 4126
 
12.6%
Distinct92
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:11.135074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length36
Mean length19.445979
Min length11

Characters and Unicode

Total characters54896
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row897 Long Airport Avenue
2nd row59 rue de l'Abbaye
3rd row27 rue du Colonel Pierre Avia
4th row78934 Hillside Dr.
5th row7734 Strong St.
ValueCountFrequency (%)
st 442
 
4.6%
c 306
 
3.2%
rue 281
 
2.9%
moralzarzal 259
 
2.7%
86 259
 
2.7%
strong 250
 
2.6%
street 216
 
2.2%
5677 180
 
1.9%
furth 135
 
1.4%
circle 135
 
1.4%
Other values (210) 7216
74.6%
2025-08-08T15:53:11.687622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6904
 
12.6%
e 3914
 
7.1%
r 3579
 
6.5%
a 2883
 
5.3%
t 2545
 
4.6%
n 2485
 
4.5%
o 2437
 
4.4%
l 1979
 
3.6%
i 1901
 
3.5%
u 1438
 
2.6%
Other values (57) 24831
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6904
 
12.6%
e 3914
 
7.1%
r 3579
 
6.5%
a 2883
 
5.3%
t 2545
 
4.6%
n 2485
 
4.5%
o 2437
 
4.4%
l 1979
 
3.6%
i 1901
 
3.5%
u 1438
 
2.6%
Other values (57) 24831
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6904
 
12.6%
e 3914
 
7.1%
r 3579
 
6.5%
a 2883
 
5.3%
t 2545
 
4.6%
n 2485
 
4.5%
o 2437
 
4.4%
l 1979
 
3.6%
i 1901
 
3.5%
u 1438
 
2.6%
Other values (57) 24831
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6904
 
12.6%
e 3914
 
7.1%
r 3579
 
6.5%
a 2883
 
5.3%
t 2545
 
4.6%
n 2485
 
4.5%
o 2437
 
4.4%
l 1979
 
3.6%
i 1901
 
3.5%
u 1438
 
2.6%
Other values (57) 24831
45.2%

ADDRESSLINE2
Categorical

High correlation  Missing 

Distinct9
Distinct (%)3.0%
Missing2521
Missing (%)89.3%
Memory size22.2 KiB
Level 3
55 
Suite 400
48 
Level 6
46 
Level 15
46 
2nd Floor
36 
Other values (4)
71 

Length

Max length11
Median length9
Mean length8.2847682
Min length7

Characters and Unicode

Total characters2502
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel 3
2nd rowSuite 101
3rd rowLevel 6
4th rowSuite 750
5th rowLevel 6

Common Values

ValueCountFrequency (%)
Level 3 55
 
1.9%
Suite 400 48
 
1.7%
Level 6 46
 
1.6%
Level 15 46
 
1.6%
2nd Floor 36
 
1.3%
Suite 101 25
 
0.9%
Suite 750 20
 
0.7%
Floor No. 4 16
 
0.6%
Suite 200 10
 
0.4%
(Missing) 2521
89.3%

Length

2025-08-08T15:53:11.872634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:11.973469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
level 147
23.7%
suite 103
16.6%
3 55
 
8.9%
floor 52
 
8.4%
400 48
 
7.7%
6 46
 
7.4%
15 46
 
7.4%
2nd 36
 
5.8%
101 25
 
4.0%
750 20
 
3.2%
Other values (3) 42
 
6.8%

Most occurring characters

ValueCountFrequency (%)
e 397
15.9%
318
12.7%
l 199
 
8.0%
0 161
 
6.4%
v 147
 
5.9%
L 147
 
5.9%
o 120
 
4.8%
S 103
 
4.1%
t 103
 
4.1%
u 103
 
4.1%
Other values (14) 704
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 397
15.9%
318
12.7%
l 199
 
8.0%
0 161
 
6.4%
v 147
 
5.9%
L 147
 
5.9%
o 120
 
4.8%
S 103
 
4.1%
t 103
 
4.1%
u 103
 
4.1%
Other values (14) 704
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 397
15.9%
318
12.7%
l 199
 
8.0%
0 161
 
6.4%
v 147
 
5.9%
L 147
 
5.9%
o 120
 
4.8%
S 103
 
4.1%
t 103
 
4.1%
u 103
 
4.1%
Other values (14) 704
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 397
15.9%
318
12.7%
l 199
 
8.0%
0 161
 
6.4%
v 147
 
5.9%
L 147
 
5.9%
o 120
 
4.8%
S 103
 
4.1%
t 103
 
4.1%
u 103
 
4.1%
Other values (14) 704
28.1%

CITY
Text

Distinct73
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:12.210068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.7530995
Min length3

Characters and Unicode

Total characters21887
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYC
2nd rowReims
3rd rowParis
4th rowPasadena
5th rowSan Francisco
ValueCountFrequency (%)
san 307
 
9.0%
madrid 304
 
8.9%
rafael 180
 
5.3%
nyc 152
 
4.4%
singapore 79
 
2.3%
new 78
 
2.3%
paris 70
 
2.0%
francisco 62
 
1.8%
bedford 61
 
1.8%
nantes 60
 
1.8%
Other values (72) 2073
60.5%
2025-08-08T15:53:12.509080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2614
 
11.9%
e 2008
 
9.2%
n 1562
 
7.1%
r 1501
 
6.9%
i 1327
 
6.1%
o 1298
 
5.9%
l 1083
 
4.9%
s 1049
 
4.8%
d 1019
 
4.7%
603
 
2.8%
Other values (37) 7823
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2614
 
11.9%
e 2008
 
9.2%
n 1562
 
7.1%
r 1501
 
6.9%
i 1327
 
6.1%
o 1298
 
5.9%
l 1083
 
4.9%
s 1049
 
4.8%
d 1019
 
4.7%
603
 
2.8%
Other values (37) 7823
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2614
 
11.9%
e 2008
 
9.2%
n 1562
 
7.1%
r 1501
 
6.9%
i 1327
 
6.1%
o 1298
 
5.9%
l 1083
 
4.9%
s 1049
 
4.8%
d 1019
 
4.7%
603
 
2.8%
Other values (37) 7823
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2614
 
11.9%
e 2008
 
9.2%
n 1562
 
7.1%
r 1501
 
6.9%
i 1327
 
6.1%
o 1298
 
5.9%
l 1083
 
4.9%
s 1049
 
4.8%
d 1019
 
4.7%
603
 
2.8%
Other values (37) 7823
35.7%

STATE
Categorical

High correlation  Missing 

Distinct16
Distinct (%)1.2%
Missing1486
Missing (%)52.6%
Memory size22.2 KiB
CA
416 
MA
190 
NY
178 
NSW
92 
Victoria
78 
Other values (11)
383 

Length

Max length13
Median length2
Mean length2.9050112
Min length2

Characters and Unicode

Total characters3884
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 416
 
14.7%
MA 190
 
6.7%
NY 178
 
6.3%
NSW 92
 
3.3%
Victoria 78
 
2.8%
PA 75
 
2.7%
CT 61
 
2.2%
BC 48
 
1.7%
NH 34
 
1.2%
Tokyo 32
 
1.1%
Other values (6) 133
 
4.7%
(Missing) 1486
52.6%

Length

2025-08-08T15:53:12.588122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 416
29.9%
ma 190
13.7%
ny 178
12.8%
nsw 92
 
6.6%
victoria 78
 
5.6%
pa 75
 
5.4%
ct 61
 
4.4%
bc 48
 
3.5%
nh 34
 
2.4%
tokyo 32
 
2.3%
Other values (8) 185
13.3%

Most occurring characters

ValueCountFrequency (%)
A 681
17.5%
C 525
13.5%
N 354
 
9.1%
M 190
 
4.9%
i 182
 
4.7%
Y 178
 
4.6%
o 168
 
4.3%
a 133
 
3.4%
W 118
 
3.0%
V 107
 
2.8%
Other values (25) 1248
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 681
17.5%
C 525
13.5%
N 354
 
9.1%
M 190
 
4.9%
i 182
 
4.7%
Y 178
 
4.6%
o 168
 
4.3%
a 133
 
3.4%
W 118
 
3.0%
V 107
 
2.8%
Other values (25) 1248
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 681
17.5%
C 525
13.5%
N 354
 
9.1%
M 190
 
4.9%
i 182
 
4.7%
Y 178
 
4.6%
o 168
 
4.3%
a 133
 
3.4%
W 118
 
3.0%
V 107
 
2.8%
Other values (25) 1248
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 681
17.5%
C 525
13.5%
N 354
 
9.1%
M 190
 
4.9%
i 182
 
4.7%
Y 178
 
4.6%
o 168
 
4.3%
a 133
 
3.4%
W 118
 
3.0%
V 107
 
2.8%
Other values (25) 1248
32.1%

POSTALCODE
Text

Missing 

Distinct73
Distinct (%)2.7%
Missing76
Missing (%)2.7%
Memory size22.2 KiB
2025-08-08T15:53:12.777056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2133236
Min length1

Characters and Unicode

Total characters14321
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10022
2nd row51100
3rd row75508
4th row90003
5th row94217
ValueCountFrequency (%)
28034 259
 
8.4%
97562 205
 
6.6%
10022 152
 
4.9%
94217 89
 
2.9%
50553 61
 
2.0%
44000 60
 
1.9%
3004 55
 
1.8%
n 53
 
1.7%
ec2 51
 
1.6%
5nt 51
 
1.6%
Other values (75) 2061
66.5%
2025-08-08T15:53:13.071203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3173
22.2%
2 1890
13.2%
1 1434
10.0%
4 1044
 
7.3%
3 1035
 
7.2%
5 990
 
6.9%
7 947
 
6.6%
9 763
 
5.3%
6 740
 
5.2%
8 712
 
5.0%
Other values (22) 1593
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3173
22.2%
2 1890
13.2%
1 1434
10.0%
4 1044
 
7.3%
3 1035
 
7.2%
5 990
 
6.9%
7 947
 
6.6%
9 763
 
5.3%
6 740
 
5.2%
8 712
 
5.0%
Other values (22) 1593
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3173
22.2%
2 1890
13.2%
1 1434
10.0%
4 1044
 
7.3%
3 1035
 
7.2%
5 990
 
6.9%
7 947
 
6.6%
9 763
 
5.3%
6 740
 
5.2%
8 712
 
5.0%
Other values (22) 1593
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3173
22.2%
2 1890
13.2%
1 1434
10.0%
4 1044
 
7.3%
3 1035
 
7.2%
5 990
 
6.9%
7 947
 
6.6%
9 763
 
5.3%
6 740
 
5.2%
8 712
 
5.0%
Other values (22) 1593
11.1%

COUNTRY
Categorical

High correlation 

Distinct19
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
USA
1004 
Spain
342 
France
314 
Australia
185 
UK
144 
Other values (14)
834 

Length

Max length11
Median length9
Mean length5.0446334
Min length2

Characters and Unicode

Total characters14241
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowFrance
3rd rowFrance
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 1004
35.6%
Spain 342
 
12.1%
France 314
 
11.1%
Australia 185
 
6.6%
UK 144
 
5.1%
Italy 113
 
4.0%
Finland 92
 
3.3%
Norway 85
 
3.0%
Singapore 79
 
2.8%
Canada 70
 
2.5%
Other values (9) 395
 
14.0%

Length

2025-08-08T15:53:13.155932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa 1004
35.6%
spain 342
 
12.1%
france 314
 
11.1%
australia 185
 
6.6%
uk 144
 
5.1%
italy 113
 
4.0%
finland 92
 
3.3%
norway 85
 
3.0%
singapore 79
 
2.8%
canada 70
 
2.5%
Other values (9) 395
 
14.0%

Most occurring characters

ValueCountFrequency (%)
a 1936
13.6%
S 1513
10.6%
n 1296
 
9.1%
A 1244
 
8.7%
U 1148
 
8.1%
i 895
 
6.3%
r 890
 
6.2%
e 738
 
5.2%
p 525
 
3.7%
l 496
 
3.5%
Other values (23) 3560
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1936
13.6%
S 1513
10.6%
n 1296
 
9.1%
A 1244
 
8.7%
U 1148
 
8.1%
i 895
 
6.3%
r 890
 
6.2%
e 738
 
5.2%
p 525
 
3.7%
l 496
 
3.5%
Other values (23) 3560
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1936
13.6%
S 1513
10.6%
n 1296
 
9.1%
A 1244
 
8.7%
U 1148
 
8.1%
i 895
 
6.3%
r 890
 
6.2%
e 738
 
5.2%
p 525
 
3.7%
l 496
 
3.5%
Other values (23) 3560
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1936
13.6%
S 1513
10.6%
n 1296
 
9.1%
A 1244
 
8.7%
U 1148
 
8.1%
i 895
 
6.3%
r 890
 
6.2%
e 738
 
5.2%
p 525
 
3.7%
l 496
 
3.5%
Other values (23) 3560
25.0%

TERRITORY
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing1074
Missing (%)38.0%
Memory size22.2 KiB
EMEA
1407 
APAC
221 
Japan
 
121

Length

Max length5
Median length4
Mean length4.0691824
Min length4

Characters and Unicode

Total characters7117
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMEA
2nd rowEMEA
3rd rowEMEA
4th rowEMEA
5th rowEMEA

Common Values

ValueCountFrequency (%)
EMEA 1407
49.8%
APAC 221
 
7.8%
Japan 121
 
4.3%
(Missing) 1074
38.0%

Length

2025-08-08T15:53:13.244485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:13.297893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
emea 1407
80.4%
apac 221
 
12.6%
japan 121
 
6.9%

Most occurring characters

ValueCountFrequency (%)
E 2814
39.5%
A 1849
26.0%
M 1407
19.8%
a 242
 
3.4%
P 221
 
3.1%
C 221
 
3.1%
J 121
 
1.7%
p 121
 
1.7%
n 121
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2814
39.5%
A 1849
26.0%
M 1407
19.8%
a 242
 
3.4%
P 221
 
3.1%
C 221
 
3.1%
J 121
 
1.7%
p 121
 
1.7%
n 121
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2814
39.5%
A 1849
26.0%
M 1407
19.8%
a 242
 
3.4%
P 221
 
3.1%
C 221
 
3.1%
J 121
 
1.7%
p 121
 
1.7%
n 121
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2814
39.5%
A 1849
26.0%
M 1407
19.8%
a 242
 
3.4%
P 221
 
3.1%
C 221
 
3.1%
J 121
 
1.7%
p 121
 
1.7%
n 121
 
1.7%
Distinct77
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:13.471189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.4413744
Min length2

Characters and Unicode

Total characters18184
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYu
2nd rowHenriot
3rd rowDa Cunha
4th rowYoung
5th rowBrown
ValueCountFrequency (%)
freyre 259
 
9.1%
nelson 204
 
7.2%
young 115
 
4.0%
frick 91
 
3.2%
brown 88
 
3.1%
yu 80
 
2.8%
hernandez 70
 
2.5%
ferguson 55
 
1.9%
king 54
 
1.9%
labrune 53
 
1.9%
Other values (68) 1774
62.4%
2025-08-08T15:53:13.739287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2297
 
12.6%
r 1850
 
10.2%
n 1769
 
9.7%
o 1355
 
7.5%
a 1137
 
6.3%
i 952
 
5.2%
s 759
 
4.2%
l 701
 
3.9%
u 647
 
3.6%
t 579
 
3.2%
Other values (35) 6138
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2297
 
12.6%
r 1850
 
10.2%
n 1769
 
9.7%
o 1355
 
7.5%
a 1137
 
6.3%
i 952
 
5.2%
s 759
 
4.2%
l 701
 
3.9%
u 647
 
3.6%
t 579
 
3.2%
Other values (35) 6138
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2297
 
12.6%
r 1850
 
10.2%
n 1769
 
9.7%
o 1355
 
7.5%
a 1137
 
6.3%
i 952
 
5.2%
s 759
 
4.2%
l 701
 
3.9%
u 647
 
3.6%
t 579
 
3.2%
Other values (35) 6138
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2297
 
12.6%
r 1850
 
10.2%
n 1769
 
9.7%
o 1355
 
7.5%
a 1137
 
6.3%
i 952
 
5.2%
s 759
 
4.2%
l 701
 
3.9%
u 647
 
3.6%
t 579
 
3.2%
Other values (35) 6138
33.8%
Distinct72
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-08-08T15:53:13.912570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.6680836
Min length3

Characters and Unicode

Total characters16001
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKwai
2nd rowPaul
3rd rowDaniel
4th rowJulie
5th rowJulie
ValueCountFrequency (%)
diego 259
 
9.0%
valarie 257
 
8.9%
julie 117
 
4.1%
michael 84
 
2.9%
sue 84
 
2.9%
juri 60
 
2.1%
maria 58
 
2.0%
elizabeth 55
 
1.9%
peter 55
 
1.9%
janine 53
 
1.8%
Other values (64) 1791
62.3%
2025-08-08T15:53:14.181003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2067
12.9%
i 1890
11.8%
a 1875
 
11.7%
r 1069
 
6.7%
n 1049
 
6.6%
l 1017
 
6.4%
o 846
 
5.3%
t 573
 
3.6%
u 505
 
3.2%
J 420
 
2.6%
Other values (33) 4690
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2067
12.9%
i 1890
11.8%
a 1875
 
11.7%
r 1069
 
6.7%
n 1049
 
6.6%
l 1017
 
6.4%
o 846
 
5.3%
t 573
 
3.6%
u 505
 
3.2%
J 420
 
2.6%
Other values (33) 4690
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2067
12.9%
i 1890
11.8%
a 1875
 
11.7%
r 1069
 
6.7%
n 1049
 
6.6%
l 1017
 
6.4%
o 846
 
5.3%
t 573
 
3.6%
u 505
 
3.2%
J 420
 
2.6%
Other values (33) 4690
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2067
12.9%
i 1890
11.8%
a 1875
 
11.7%
r 1069
 
6.7%
n 1049
 
6.6%
l 1017
 
6.4%
o 846
 
5.3%
t 573
 
3.6%
u 505
 
3.2%
J 420
 
2.6%
Other values (33) 4690
29.3%

DEALSIZE
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
Medium
1384 
Small
1282 
Large
157 

Length

Max length6
Median length5
Mean length5.4902586
Min length5

Characters and Unicode

Total characters15499
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowSmall
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 1384
49.0%
Small 1282
45.4%
Large 157
 
5.6%

Length

2025-08-08T15:53:14.275299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-08T15:53:14.348232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 1384
49.0%
small 1282
45.4%
large 157
 
5.6%

Most occurring characters

ValueCountFrequency (%)
m 2666
17.2%
l 2564
16.5%
e 1541
9.9%
a 1439
9.3%
i 1384
8.9%
d 1384
8.9%
M 1384
8.9%
u 1384
8.9%
S 1282
8.3%
L 157
 
1.0%
Other values (2) 314
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 2666
17.2%
l 2564
16.5%
e 1541
9.9%
a 1439
9.3%
i 1384
8.9%
d 1384
8.9%
M 1384
8.9%
u 1384
8.9%
S 1282
8.3%
L 157
 
1.0%
Other values (2) 314
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 2666
17.2%
l 2564
16.5%
e 1541
9.9%
a 1439
9.3%
i 1384
8.9%
d 1384
8.9%
M 1384
8.9%
u 1384
8.9%
S 1282
8.3%
L 157
 
1.0%
Other values (2) 314
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 2666
17.2%
l 2564
16.5%
e 1541
9.9%
a 1439
9.3%
i 1384
8.9%
d 1384
8.9%
M 1384
8.9%
u 1384
8.9%
S 1282
8.3%
L 157
 
1.0%
Other values (2) 314
 
2.0%

Interactions

2025-08-08T15:53:04.698579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.224304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-08T15:53:03.433372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.136367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.902489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.425165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.078623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.826903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.655406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.528774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.230806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:05.009317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.527075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.170122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.952909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.781831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.651110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.326727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:05.103265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.619017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.277039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.077711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.890707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.748523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.430974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:05.211071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.732887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.374025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.181555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.091642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.844353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.519398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:05.303501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:00.816920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:01.464416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:02.304426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.186320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:03.931265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-08T15:53:04.611921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-08T15:53:14.495400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ADDRESSLINE2COUNTRYDEALSIZEMONTH_IDMSRPORDERLINENUMBERORDERNUMBERPRICEEACHPRODUCTLINEQTR_IDQUANTITYORDEREDSALESSTATESTATUSTERRITORYYEAR_ID
ADDRESSLINE21.0000.9920.0000.4610.0000.0000.5320.0000.3160.4020.0630.0000.9920.4740.9920.529
COUNTRY0.9921.0000.0000.2530.0000.0000.2570.0180.1570.2360.0410.0000.9960.2070.9310.224
DEALSIZE0.0000.0001.0000.0410.4840.0440.0370.4810.1480.0350.3650.8980.0310.0660.0000.035
MONTH_ID0.4610.2530.0411.0000.0080.032-0.0120.0110.0380.999-0.026-0.0020.3080.2830.1660.414
MSRP0.0000.0000.4840.0081.000-0.014-0.0100.7560.3190.0000.0180.6650.0000.0000.0000.000
ORDERLINENUMBER0.0000.0000.0440.032-0.0141.000-0.048-0.0310.0330.015-0.021-0.0470.0000.0000.0000.000
ORDERNUMBER0.5320.2570.037-0.012-0.010-0.0481.000-0.0040.0000.8330.0430.0210.3360.3400.2120.954
PRICEEACH0.0000.0180.4810.0110.756-0.031-0.0041.0000.1460.0230.0060.7880.0000.0340.0050.016
PRODUCTLINE0.3160.1570.1480.0380.3190.0330.0000.1461.0000.0200.0000.1120.1800.0930.1100.005
QTR_ID0.4020.2360.0350.9990.0000.0150.8330.0230.0201.0000.1390.0210.3560.2480.0680.380
QUANTITYORDERED0.0630.0410.365-0.0260.018-0.0210.0430.0060.0000.1391.0000.5380.1080.1910.0000.193
SALES0.0000.0000.898-0.0020.665-0.0470.0210.7880.1120.0210.5381.0000.0000.0780.0350.056
STATE0.9920.9960.0310.3080.0000.0000.3360.0000.1800.3560.1080.0001.0000.2790.9940.314
STATUS0.4740.2070.0660.2830.0000.0000.3400.0340.0930.2480.1910.0780.2791.0000.1040.307
TERRITORY0.9920.9310.0000.1660.0000.0000.2120.0050.1100.0680.0000.0350.9940.1041.0000.065
YEAR_ID0.5290.2240.0350.4140.0000.0000.9540.0160.0050.3800.1930.0560.3140.3070.0651.000

Missing values

2025-08-08T15:53:05.519086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-08T15:53:05.720035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-08T15:53:05.994318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ORDERNUMBERQUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESORDERDATESTATUSQTR_IDMONTH_IDYEAR_IDPRODUCTLINEMSRPPRODUCTCODECUSTOMERNAMEPHONEADDRESSLINE1ADDRESSLINE2CITYSTATEPOSTALCODECOUNTRYTERRITORYCONTACTLASTNAMECONTACTFIRSTNAMEDEALSIZE
0101073095.7022871.002/24/2003 0:00Shipped122003Motorcycles95S10_1678Land of Toys Inc.2125557818897 Long Airport AvenueNaNNYCNY10022USANaNYuKwaiSmall
1101213481.3552765.905/7/2003 0:00Shipped252003Motorcycles95S10_1678Reims Collectables26.47.155559 rue de l'AbbayeNaNReimsNaN51100FranceEMEAHenriotPaulSmall
2101344194.7423884.347/1/2003 0:00Shipped372003Motorcycles95S10_1678Lyon Souveniers+33 1 46 62 755527 rue du Colonel Pierre AviaNaNParisNaN75508FranceEMEADa CunhaDanielMedium
3101454583.2663746.708/25/2003 0:00Shipped382003Motorcycles95S10_1678Toys4GrownUps.com626555726578934 Hillside Dr.NaNPasadenaCA90003USANaNYoungJulieMedium
41015949100.00145205.2710/10/2003 0:00Shipped4102003Motorcycles95S10_1678Corporate Gift Ideas Co.65055513867734 Strong St.NaNSan FranciscoCANaNUSANaNBrownJulieMedium
5101683696.6613479.7610/28/2003 0:00Shipped4102003Motorcycles95S10_1678Technics Stores Inc.65055568099408 Furth CircleNaNBurlingameCA94217USANaNHiranoJuriMedium
6101802986.1392497.7711/11/2003 0:00Shipped4112003Motorcycles95S10_1678Daedalus Designs Imports20.16.1555184, chausse de TournaiNaNLilleNaN59000FranceEMEARanceMartineSmall
71018848100.0015512.3211/18/2003 0:00Shipped4112003Motorcycles95S10_1678Herkku Gifts+47 2267 3215Drammen 121, PR 744 SentrumNaNBergenNaNN 5804NorwayEMEAOeztanVeyselMedium
8102012298.5722168.5412/1/2003 0:00Shipped4122003Motorcycles95S10_1678Mini Wheels Co.65055557875557 North Pendale StreetNaNSan FranciscoCANaNUSANaNMurphyJulieSmall
91021141100.00144708.441/15/2004 0:00Shipped112004Motorcycles95S10_1678Auto Canal Petit(1) 47.55.655525, rue LauristonNaNParisNaN75016FranceEMEAPerrierDominiqueMedium
ORDERNUMBERQUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESORDERDATESTATUSQTR_IDMONTH_IDYEAR_IDPRODUCTLINEMSRPPRODUCTCODECUSTOMERNAMEPHONEADDRESSLINE1ADDRESSLINE2CITYSTATEPOSTALCODECOUNTRYTERRITORYCONTACTLASTNAMECONTACTFIRSTNAMEDEALSIZE
2813102933260.0611921.929/9/2004 0:00Shipped392004Ships54S72_3212Amica Models & Co.011-4988555Via Monte Bianco 34NaNTorinoNaN10100ItalyEMEAAccortiPaoloSmall
2814103063559.5162082.8510/14/2004 0:00Shipped4102004Ships54S72_3212AV Stores, Co.(171) 555-1555Fauntleroy CircusNaNManchesterNaNEC2 5NTUKEMEAAshworthVictoriaSmall
2815103154055.6952227.6010/29/2004 0:00Shipped4102004Ships54S72_3212La Rochelle Gifts40.67.855567, rue des Cinquante OtagesNaNNantesNaN44000FranceEMEALabruneJanineSmall
2816103273786.7443209.3811/10/2004 0:00Resolved4112004Ships54S72_3212Danish Wholesale Imports31 12 3555Vinb'ltet 34NaNKobenhavnNaN1734DenmarkEMEAPetersenJytteMedium
2817103374297.1654080.7211/21/2004 0:00Shipped4112004Ships54S72_3212Classic Legends Inc.21255584935905 Pompton St.Suite 750NYCNY10022USANaNHernandezMariaMedium
28181035020100.00152244.4012/2/2004 0:00Shipped4122004Ships54S72_3212Euro Shopping Channel(91) 555 94 44C/ Moralzarzal, 86NaNMadridNaN28034SpainEMEAFreyreDiegoSmall
28191037329100.0013978.511/31/2005 0:00Shipped112005Ships54S72_3212Oulu Toy Supplies, Inc.981-443655Torikatu 38NaNOuluNaN90110FinlandEMEAKoskitaloPirkkoMedium
28201038643100.0045417.573/1/2005 0:00Resolved132005Ships54S72_3212Euro Shopping Channel(91) 555 94 44C/ Moralzarzal, 86NaNMadridNaN28034SpainEMEAFreyreDiegoMedium
2821103973462.2412116.163/28/2005 0:00Shipped132005Ships54S72_3212Alpha Cognac61.77.65551 rue Alsace-LorraineNaNToulouseNaN31000FranceEMEARouletAnnetteSmall
2822104144765.5293079.445/6/2005 0:00On Hold252005Ships54S72_3212Gifts4AllAges.com61755595558616 Spinnaker Dr.NaNBostonMA51003USANaNYoshidoJuriMedium